BiocNeighbors 1.10.0
The BiocNeighbors package provides several algorithms for approximate neighbor searches:
These methods complement the exact algorithms described previously.
Again, it is straightforward to switch from one algorithm to another by simply changing the BNPARAM argument in findKNN and queryKNN.
We perform the k-nearest neighbors search with the Annoy algorithm by specifying BNPARAM=AnnoyParam().
nobs <- 10000
ndim <- 20
data <- matrix(runif(nobs*ndim), ncol=ndim)
fout <- findKNN(data, k=10, BNPARAM=AnnoyParam())
head(fout$index)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 6839 3564 8970 8494 2514 3375 8041 4673 2809 6557
## [2,] 3762 8510 1930 1709 7576 7814 9139 3511 1450 2967
## [3,] 3446 3654 4728 1638 2751 2345 3632 1000 7965 4392
## [4,] 8817 6584 8180 59 3050 2645 1465 7738 9324 6834
## [5,] 9889 1561 3571 9 4544 2686 9911 6703 2909 9965
## [6,] 2855 5428 5531 5760 771 7536 3368 6385 670 2652
head(fout$distance)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 0.9921864 0.9957834 0.9994216 1.0113053 1.0138078 1.0223548 1.0240149
## [2,] 0.8645985 0.9447311 0.9579430 0.9840193 0.9866839 0.9876871 0.9897641
## [3,] 0.9259275 0.9518557 0.9632121 0.9735848 0.9742126 0.9901797 0.9999163
## [4,] 0.8075849 0.8929428 0.9125759 0.9290096 0.9432879 0.9578164 0.9866946
## [5,] 0.9659729 0.9993480 1.0383309 1.1012558 1.1051797 1.1090480 1.1115953
## [6,] 0.7669217 0.9034132 0.9614732 0.9849571 0.9958620 0.9966433 1.0280401
## [,8] [,9] [,10]
## [1,] 1.0260382 1.0628729 1.072670
## [2,] 0.9969519 0.9970498 1.003801
## [3,] 1.0107327 1.0114325 1.023189
## [4,] 0.9878797 1.0132945 1.048254
## [5,] 1.1497359 1.1527627 1.160382
## [6,] 1.0282890 1.0388981 1.039620
We can also identify the k-nearest neighbors in one dataset based on query points in another dataset.
nquery <- 1000
ndim <- 20
query <- matrix(runif(nquery*ndim), ncol=ndim)
qout <- queryKNN(data, query, k=5, BNPARAM=AnnoyParam())
head(qout$index)
## [,1] [,2] [,3] [,4] [,5]
## [1,] 4718 2150 4276 4908 1207
## [2,] 4228 2541 8287 1392 6196
## [3,] 3533 7801 6299 1937 7967
## [4,] 9597 9572 5819 6116 5156
## [5,] 8190 367 8620 223 5560
## [6,] 4112 6999 9241 1377 9643
head(qout$distance)
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.8218788 0.8370792 0.8468705 0.9359561 0.9615508
## [2,] 0.8191825 0.8814184 0.9263154 0.9368848 0.9414537
## [3,] 0.7210571 0.8295944 0.8740436 0.9274628 0.9452164
## [4,] 0.8753332 0.8821529 0.8910749 0.9699543 1.0150330
## [5,] 0.8536909 0.8577128 0.8752092 0.8840870 0.8868778
## [6,] 1.0393667 1.0482323 1.1029489 1.1369380 1.1387308
It is similarly easy to use the HNSW algorithm by setting BNPARAM=HnswParam().
Most of the options described for the exact methods are also applicable here. For example:
subset to identify neighbors for a subset of points.get.distance to avoid retrieving distances when unnecessary.BPPARAM to parallelize the calculations across multiple workers.BNINDEX to build the forest once for a given data set and re-use it across calls.The use of a pre-built BNINDEX is illustrated below:
pre <- buildIndex(data, BNPARAM=AnnoyParam())
out1 <- findKNN(BNINDEX=pre, k=5)
out2 <- queryKNN(BNINDEX=pre, query=query, k=2)
Both Annoy and HNSW perform searches based on the Euclidean distance by default.
Searching by Manhattan distance is done by simply setting distance="Manhattan" in AnnoyParam() or HnswParam().
Users are referred to the documentation of each function for specific details on the available arguments.
Both Annoy and HNSW generate indexing structures - a forest of trees and series of graphs, respectively -
that are saved to file when calling buildIndex().
By default, this file is located in tempdir()1 On HPC file systems, you can change TEMPDIR to a location that is more amenable to concurrent access. and will be removed when the session finishes.
AnnoyIndex_path(pre)
## [1] "/tmp/RtmpfiYBOk/file2a908cf0048a2.idx"
If the index is to persist across sessions, the path of the index file can be directly specified in buildIndex.
This can be used to construct an index object directly using the relevant constructors, e.g., AnnoyIndex(), HnswIndex().
However, it becomes the responsibility of the user to clean up any temporary indexing files after calculations are complete.
sessionInfo()
## R version 4.1.0 (2021-05-18)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.13-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.13-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] BiocNeighbors_1.10.0 knitr_1.33 BiocStyle_2.20.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.6 magrittr_2.0.1 BiocGenerics_0.38.0
## [4] BiocParallel_1.26.0 lattice_0.20-44 R6_2.5.0
## [7] rlang_0.4.11 stringr_1.4.0 tools_4.1.0
## [10] parallel_4.1.0 grid_4.1.0 xfun_0.23
## [13] jquerylib_0.1.4 htmltools_0.5.1.1 yaml_2.2.1
## [16] digest_0.6.27 bookdown_0.22 Matrix_1.3-3
## [19] BiocManager_1.30.15 S4Vectors_0.30.0 sass_0.4.0
## [22] evaluate_0.14 rmarkdown_2.8 stringi_1.6.2
## [25] compiler_4.1.0 bslib_0.2.5.1 stats4_4.1.0
## [28] jsonlite_1.7.2